NSF/SBE-BSF: Neural patterns underlying the development of planning in action production and anticipation in action perception

运动技能的一个显著方面是灵活和有目的地使用各种物体计划行动的能力。我们通过在脑海中规划与最终目标的初始接触来实现这一点，即使最终目标需要多个步骤才能实现。因此，行动计划的过程涉及感知、认知和运动行为的整合。据推测，当我们在观察他人的行为时预测他们的目标时，也会发生类似的整合过程。先前的研究表明，行动计划始于婴儿期，并随着年龄的增长而改善，但人们对伴随这些与年龄相关的改善的大脑活动知之甚少。这项跨学科的研究结合了发展心理学、神经科学和计算机科学的概念、方法和分析技术，以了解儿童在行动计划方面与年龄相关的变化：1)执行一个有多个步骤的目标的行动；2)当他们观察其他人执行该行动时。该研究采用了一种新颖的儿童记录方法组合：视频、眼动追踪、运动追踪和脑电图（EEG）将同时记录。这些方法和数据将通过开放共享研究视频和数据库中的生理数据来推进该领域的发展。算法和分析技术将在开放科学框架中共享。研究人员将行为测量、神经活动记录和机器学习技术结合起来，以了解儿童和成人如何1)执行涉及预期最终目标的复杂运动任务，2)被动观察他人执行涉及预期最终目标的多步骤行动计划的任务。动作表现的分析将集中在不同计划阶段行为的神经关联上，并将评估开始动作之前的神经活动是否可以预测幼儿计划能力的试验到试验的行为差异。对行为观察的分析将调查在观察他人的行为时神经活动的年龄差异，这些行为有或没有显示出长期计划的证据。研究人员还将比较被动动作观察和动作表现期间预期的神经生理特征。这些发现将有助于我们理解整个儿童时期行动计划的发展。多模态记录方法和先进的多元分析技术将为STEM和其他学科的研究铺平道路，探索儿童大脑和行为的发展变化。该奖项是NSF/BSF经济学和心理学合作机会的一部分。

A remarkable aspect of motor skill is the ability to plan actions flexibly and purposefully using a variety of objects. We achieve this by planning our initial contact with the object with the end goal in mind, even when the end goal requires multiple steps to be achieved. Thus, the process of action planning involves integration of perception, cognition, and motor behavior. Presumably, a similar integrative process occurs when we anticipate other people's goals while observing them perform actions. Previous work shows that action planning begins in infancy and improves with age, but little is known about the accompanying brain activity that underlies these age-related improvements. This interdisciplinary study combines concepts, methods, and analytic techniques from developmental psychology, neuroscience, and computer science to understand age-related changes in action planning while children 1) perform an action with multiple steps to the goal and 2) while they observe someone else perform the action. The research uses a novel combination of recording methods for children: Video, eye tracking, motion tracking, and electroencephalography (EEG) will be recorded simultaneously. The methods and data will advance the field through open sharing of the research videos and physiological data in the Databrary repository. Algorithms and analysis techniques will be shared in the Open Science Framework. The investigators combine behavioral measures, neural activity recordings, and machine-learning techniques in order to understand how children and adults 1) perform complex motor tasks that involve anticipation of the end-goal and 2) passively observe others performing tasks that involve multi-step action planning in anticipation of the end-goal. Analyses of action performance will focus on the neural correlates of behavior at different stages of planning and will assess whether neural activity prior to beginning a movement can predict trial-to-trial behavioral variability in young children's ability to plan. Analyses of action observation will investigate age differences in neural activity while observing others performing actions that do, or do not, show evidence of long-range planning. The investigators will also compare the neurophysiological signatures of anticipation during passive action observation and during action performance. These findings will inform our understanding of the development of action planning across childhood. The multi-modal recording methods and the advanced multivariate analytic techniques will pave the way for research in STEM and other disciplines to explore developmental changes in children's brain and behavior.This award is made as part of the NSF/BSF Opportunity for Collaborations in Economics and Psychology.